no code implementations • 17 Jun 2025 • Zhongzheng Qiao, Chenghao Liu, Yiming Zhang, Ming Jin, Quang Pham, Qingsong Wen, P. N. Suganthan, Xudong Jiang, Savitha Ramasamy
Time series foundation models (TSFMs) demonstrate impressive zero-shot performance for time series forecasting.
1 code implementation • 28 May 2025 • Tianxiang Zhan, Ming Jin, Yuanpeng He, Yuxuan Liang, Yong Deng, Shirui Pan
Recurring concept drift, a type of concept drift in which previously observed data patterns reappear after some time, is one of the most prevalent types of concept drift in time series.
no code implementations • 21 May 2025 • Shangding Gu, Donghao Ying, Ming Jin, Yu Joe Lu, Jun Wang, Javad Lavaei, Costas Spanos
In contrast to existing methods that rely on adjusting model parameters, MFL leverages a lightweight reverse model to iteratively search for optimal inputs, enabling efficient adaptation to new objectives under deployment constraints.
1 code implementation • 5 May 2025 • Yunfeng Ge, Jiawei Li, Yiji Zhao, Haomin Wen, Zhao Li, Meikang Qiu, Hongyan Li, Ming Jin, Shirui Pan
Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series datasets across domains.
1 code implementation • 12 Mar 2025 • Yuxuan Liang, Haomin Wen, Yutong Xia, Ming Jin, Bin Yang, Flora Salim, Qingsong Wen, Shirui Pan, Gao Cong
Spatio-Temporal (ST) data science, which includes sensing, managing, and mining large-scale data across space and time, is fundamental to understanding complex systems in domains such as urban computing, climate science, and intelligent transportation.
no code implementations • 11 Mar 2025 • Bilgehan Sel, Dingcheng Li, Phillip Wallis, Vaishakh Keshava, Ming Jin, Siddhartha Reddy Jonnalagadda
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks, but ensuring their safety and alignment with human values remains crucial.
no code implementations • 27 Feb 2025 • Shangding Gu, Laixi Shi, Muning Wen, Ming Jin, Eric Mazumdar, Yuejie Chi, Adam Wierman, Costas Spanos
Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment sequential interactions.
no code implementations • 26 Feb 2025 • Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen
Time series data are foundational in finance, healthcare, and energy domains.
1 code implementation • 20 Feb 2025 • Juntong Ni, Zewen Liu, Shiyu Wang, Ming Jin, Wei Jin
Based on this observation, we introduce TimeDistill, a cross-architecture KD framework that transfers these patterns from teacher models (e. g., Transformers, CNNs) to MLP.
no code implementations • 6 Feb 2025 • Siru Zhong, Weilin Ruan, Ming Jin, Huan Li, Qingsong Wen, Yuxuan Liang
Recent advancements in time series forecasting have explored augmenting models with text or vision modalities to improve accuracy.
no code implementations • 3 Feb 2025 • Yaxuan Kong, Yiyuan Yang, Shiyu Wang, Chenghao Liu, Yuxuan Liang, Ming Jin, Stefan Zohren, Dan Pei, Yan Liu, Qingsong Wen
Understanding time series data is crucial for multiple real-world applications.
no code implementations • 27 Jan 2025 • Faizan Manzoor, Vanshaj Khattar, Akila Herath, Clifton Black, Matthew C Nielsen, Junho Hong, Chen-Ching Liu, Ming Jin
The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year.
no code implementations • 23 Jan 2025 • Bilgehan Sel, Ruoxi Jia, Ming Jin
Large language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate.
no code implementations • 20 Dec 2024 • Junteng Yao, Tuo Wu, Liaoshi Zhou, Ming Jin, Cunhua Pan, Maged Elkashlan, Fumiyuki Adachi, George K. Karagiannidis, Naofal Al-Dhahir, Chau Yuen
In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs).
1 code implementation • 9 Dec 2024 • Myeongseob Ko, Henry Li, Zhun Wang, Jonathan Patsenker, Jiachen T. Wang, Qinbin Li, Ming Jin, Dawn Song, Ruoxi Jia
Driven by these concerns, machine unlearning has become crucial to effectively purge undesirable knowledge from models.
no code implementations • 5 Dec 2024 • Meiling Huang, Ming Jin, Ning li
This framework elucidates how AI shifts the locus of creative advantage from specialized expertise to broader cognitive adaptability.
no code implementations • 14 Nov 2024 • Junteng Yao, Liangxiao Xin, Tuo Wu, Ming Jin, Kai-Kit Wong, Chau Yuen, Hyundong Shin
This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas' positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden.
no code implementations • 13 Nov 2024 • Junteng Yao, Ming Jin, Tuo Wu, Maged Elkashlan, Chau Yuen, Kai-Kit Wong, George K. Karagiannidis, Hyundong Shin
Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity.
no code implementations • 7 Nov 2024 • Xinxing Zhou, Jiaqi Ye, Shubao Zhao, Ming Jin, Chengyi Yang, Yanlong Wen, Xiaojie Yuan
The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting models.
no code implementations • 26 Oct 2024 • Mohammad Beigi, Sijia Wang, Ying Shen, Zihao Lin, Adithya Kulkarni, Jianfeng He, Feng Chen, Ming Jin, Jin-Hee Cho, Dawei Zhou, Chang-Tien Lu, Lifu Huang
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications.
2 code implementations • 21 Oct 2024 • Shiyu Wang, Jiawei Li, Xiaoming Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Shengtong Ju, Zhixuan Chu, Ming Jin
Specifically, we introduce a general-purpose TSPM that processes multi-scale time series using (1) multi-resolution time imaging (MRTI), (2) time image decomposition (TID), (3) multi-scale mixing (MCM), and (4) multi-resolution mixing (MRM) to extract comprehensive temporal patterns.
1 code implementation • 16 Oct 2024 • Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin, Shirui Pan
In this work, we examine two common TSFM architectures, encoder-only and decoder-only Transformers, and investigate their scaling behavior on both ID and OOD data.
no code implementations • 24 Sep 2024 • Ming Jin, Danni Zhang, Gangming Zhao, Changde Du, Jinpeng Li
While numerous domain adaptation (DA) approaches have been proposed in recent years to address this issue, their reliance on large amounts of target data for calibration restricts them to offline scenarios, rendering them unsuitable for real-time applications.
1 code implementation • 24 Sep 2024 • Xiaoming Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, Ming Jin
However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capable forecasting models in real-world applications.
no code implementations • 24 Sep 2024 • Mushen Lin, Fenggang Yan, Lingda Ren, Xiangtian Meng, Maria Greco, Fulvio Gini, Ming Jin
However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to directly establish the association between radar measurements and real targets.
no code implementations • 24 Sep 2024 • Xinxing Zhou, Jiaqi Ye, Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Yanlong Wen, Xiaojie Yuan
In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning.
no code implementations • 10 Sep 2024 • Hoang Anh Just, Mahavir Dabas, Lifu Huang, Ming Jin, Ruoxi Jia
This approach allows the model to gain a deeper understanding of the problem's context and identify the most effective solution path during the inference stage.
no code implementations • 27 Aug 2024 • Vanshaj Khattar, Ming Jin
Offline reinforcement learning (RL) is a promising approach for many control applications but faces challenges such as limited data coverage and value function overestimation.
no code implementations • 24 Aug 2024 • Junteng Yao, Jianchao Zheng, Tuo Wu, Ming Jin, Chau Yuen, Kai-Kit Wong, Fumiyuki Adachi
This correspondence investigates the novel fluid antenna system (FAS) technology, combining with reconfigurable intelligent surface (RIS) for wireless communications, where a base station (BS) communicates with a FAS-enabled user with the assistance of a RIS.
no code implementations • 21 Aug 2024 • Tao Jiang, Ming Jin, Qinghua Guo, Yinhong Liu, Yaming Li
Integrating device-to-device (D2D) communication into cellular networks can significantly reduce the transmission burden on base stations (BSs).
no code implementations • 17 Aug 2024 • Junteng Yao, Liaoshi Zhou, Tuo Wu, Ming Jin, Chongwen Huang, Chau Yuen
We introduce an alternating optimization (AO) algorithm incorporating majorization-minimization (MM), successive convex approximation (SCA), and sequential rank-one constraint relaxation (SRCR) to tackle the non-convex challenges inherent in these systems.
no code implementations • 13 Aug 2024 • Dongyuan Li, Shiyin Tan, Ying Zhang, Ming Jin, Shirui Pan, Manabu Okumura, Renhe Jiang
Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification).
no code implementations • 25 Jul 2024 • Hyunin Lee, Chanwoo Park, David Abel, Ming Jin
Black swan events are statistically rare occurrences that carry extremely high risks.
1 code implementation • 19 Jul 2024 • Hoang Anh Just, Ming Jin, Anit Sahu, Huy Phan, Ruoxi Jia
Reinforcement learning from human feedback plays a crucial role in aligning language models towards human preferences, traditionally represented through comparisons between pairs or sets of responses within a given context.
no code implementations • 16 Jul 2024 • Liaoshi Zhou, Junteng Yao, Tuo Wu, Ming Jin, Chau Yuen, Fumiyuki Adachi
Unlike traditional SWIPT systems with fixed-position antennas (FPAs), our FA-assisted system enables dynamic reconfiguration of the radio propagation environment by adjusting the positions of FAs.
no code implementations • 11 Jul 2024 • Junteng Yao, Xiazhi Lai, Kangda Zhi, Tuo Wu, Ming Jin, Cunhua Pan, Maged Elkashlan, Chau Yuen, Kai-Kit Wong
Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method.
1 code implementation • 25 Jun 2024 • Jianfeng He, Runing Yang, Linlin Yu, Changbin Li, Ruoxi Jia, Feng Chen, Ming Jin, Chang-Tien Lu
Text summarization, a key natural language generation (NLG) task, is vital in various domains.
no code implementations • 17 Jun 2024 • Mohammad Beigi, Ying Shen, Runing Yang, Zihao Lin, Qifan Wang, Ankith Mohan, Jianfeng He, Ming Jin, Chang-Tien Lu, Lifu Huang
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinations.
no code implementations • 11 Jun 2024 • Yi Zeng, Xuelin Yang, Li Chen, Cristian Canton Ferrer, Ming Jin, Michael I. Jordan, Ruoxi Jia
To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set.
1 code implementation • 31 May 2024 • Shangding Gu, Laixi Shi, Yuhao Ding, Alois Knoll, Costas Spanos, Adam Wierman, Ming Jin
Safe reinforcement learning (RL) is crucial for deploying RL agents in real-world applications, as it aims to maximize long-term rewards while satisfying safety constraints.
no code implementations • 26 May 2024 • Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei, Ming Jin
Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience.
1 code implementation • 26 May 2024 • Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang, QIngwei Lin, Alois Knoll, Ming Jin
In numerous reinforcement learning (RL) problems involving safety-critical systems, a key challenge lies in balancing multiple objectives while simultaneously meeting all stringent safety constraints.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
1 code implementation • 25 May 2024 • Hyunin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi
Real-time inference is a challenge of real-world reinforcement learning due to temporal differences in time-varying environments: the system collects data from the past, updates the decision model in the present, and deploys it in the future.
no code implementations • 21 May 2024 • Bilgehan Sel, Priya Shanmugasundaram, Mohammad Kachuee, Kun Zhou, Ruoxi Jia, Ming Jin
Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering.
no code implementations • 18 May 2024 • Ming Jin
Operating safely and reliably despite continual distribution shifts is vital for high-stakes machine learning applications.
3 code implementations • 2 May 2024 • Shangding Gu, Bilgehan Sel, Yuhao Ding, Lu Wang, QIngwei Lin, Ming Jin, Alois Knoll
Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications.
2 code implementations • 29 Apr 2024 • Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen
Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks.
2 code implementations • 21 Mar 2024 • Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.
no code implementations • 15 Mar 2024 • Junteng Yao, Tuo Wu, Ming Jin, Cunhua Pan, Quanzhong Li, Jinhong Yuan
This paper investigates covert data transmission within a multiple-input multiple-output (MIMO) over-the-air computation (AirComp) network, where sensors transmit data to the access point (AP) while guaranteeing covertness to the warden (Willie).
1 code implementation • 13 Mar 2024 • Shangding Gu, Alois Knoll, Ming Jin
The development of Large Language Models (LLMs) often confronts challenges stemming from the heavy reliance on human annotators in the reinforcement learning with human feedback (RLHF) framework, or the frequent and costly external queries tied to the self-instruct paradigm.
no code implementations • 1 Mar 2024 • Junteng Yao, Liaoshi Zhou, Tuo Wu, Ming Jin, Cunhua Pan, Maged Elkashlan, Kai-Kit Wong
This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna.
1 code implementation • 18 Feb 2024 • Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang
In long-term time series forecasting (LTSF) tasks, an increasing number of models have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their dynamical structures.
1 code implementation • CVPR 2024 • Myeongseob Ko, Feiyang Kang, Weiyan Shi, Ming Jin, Zhou Yu, Ruoxi Jia
Inspired by this, we introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point.
2 code implementations • 5 Feb 2024 • Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen
Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications.
no code implementations • 11 Jan 2024 • Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa T. Phan, Yi-Ping Phoebe Chen, Shirui Pan, Wei Xiang
However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values.
no code implementations • 10 Jan 2024 • Shubao Zhao, Ming Jin, Zhaoxiang Hou, Chengyi Yang, Zengxiang Li, Qingsong Wen, Yi Wang
Time series forecasting is crucial and challenging in the real world.
Ranked #18 on
Time Series Forecasting
on ETTh1 (336) Multivariate
no code implementations • 28 Dec 2023 • Padmaksha Roy, Tyler Cody, Himanshu Singhal, Kevin Choi, Ming Jin
Domain generalization focuses on leveraging knowledge from multiple related domains with ample training data and labels to enhance inference on unseen in-distribution (IN) and out-of-distribution (OOD) domains.
6 code implementations • 16 Oct 2023 • Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, XiaoLi Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
no code implementations • 11 Oct 2023 • Junteng Yao, Tuo Wu, Xiazhi Lai, Ming Jin, Cunhua Pan, Maged Elkashlan, Kai-Kit Wong
Our objective is to maximize the average monitoring rate, whose expression involves the integral of the first-order Marcum $Q$ function.
3 code implementations • 3 Oct 2023 • Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
Ranked #1 on
Time Series Forecasting
on ETTh1 (48)
1 code implementation • ICCV 2023 • Myeongseob Ko, Ming Jin, Chenguang Wang, Ruoxi Jia
Furthermore, our enhanced attacks outperform the baseline across multiple models and datasets, with the weakly supervised attack demonstrating an average-case performance improvement of $17\%$ and being at least $7$X more effective at low false-positive rates.
1 code implementation • NeurIPS 2023 • Hyunin Lee, Yuhao Ding, Jongmin Lee, Ming Jin, Javad Lavaei, Somayeh Sojoudi
In the context of the time-desynchronized environment, however, the agent at time $t_{k}$ allocates $\Delta t$ for trajectory generation and training, subsequently moves to the next episode at $t_{k+1}=t_{k}+\Delta t$.
no code implementations • 1 Sep 2023 • Yiwen Mao, Dawei Gao, Qinghua Guo, Ming Jin
This work deals with directional of arrival (DOA) estimation with a large antenna array.
no code implementations • 20 Aug 2023 • Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin
Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to external modi operandi involving halting, modifying, and then resuming the generation process to boost Large Language Models' (LLMs) reasoning capacities.
no code implementations • 20 Aug 2023 • Ming Jin, Bilgehan Sel, Fnu Hardeep, Wotao Yin
This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs).
1 code implementation • 10 Aug 2023 • Siqiao Xue, Fan Zhou, Yi Xu, Ming Jin, Qingsong Wen, Hongyan Hao, Qingyang Dai, Caigao Jiang, Hongyu Zhao, Shuo Xie, Jianshan He, James Zhang, Hongyuan Mei
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain.
1 code implementation • 17 Jul 2023 • Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xiang
To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time series anomaly detection.
1 code implementation • 7 Jul 2023 • Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
1 code implementation • 16 Jun 2023 • Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
no code implementations • 21 May 2023 • Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan, Philip S. Yu
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
no code implementations • 11 May 2023 • Ming Jin, Guangsi Shi, Yuan-Fang Li, Qingsong Wen, Bo Xiong, Tian Zhou, Shirui Pan
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
1 code implementation • 28 Apr 2023 • Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia
(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets.
no code implementations • 24 Apr 2023 • Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification
Knowledge Graph Completion
+3
no code implementations • 15 Apr 2023 • Ahmad Faraz Khan, Xinran Wang, Qi Le, Zain ul Abdeen, Azal Ahmad Khan, Haider Ali, Ming Jin, Jie Ding, Ali R. Butt, Ali Anwar
Our approach enhances the personalized model appeal for self-aware clients with high-quality data leading to their active and consistent participation.
1 code implementation • 10 Mar 2023 • Mohammad S. Ramadan, Ahmad Al-Tawaha, Mohamed Shouman, Ahmed Atallah, Ming Jin
This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function, dependent on the process noise density, in a "self-approximating" fashion, eliminating the need for ordering or set-membership tests.
no code implementations • 4 Dec 2022 • Vanshaj Khattar, Ming Jin
Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions.
no code implementations • 2 Dec 2022 • Ming Jin, Vanshaj Khattar, Harshal Kaushik, Bilgehan Sel, Ruoxi Jia
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension.
no code implementations • 19 Nov 2022 • Yuhao Ding, Ming Jin, Javad Lavaei
We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).
no code implementations • 9 Nov 2022 • Dawei Gao, Qinghua Guo, Ming Jin, Guisheng Liao, Yonina C. Eldar
Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.
no code implementations • 1 Nov 2022 • Enwei Zhu, Yiyang Liu, Ming Jin, Jinpeng Li
However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly.
no code implementations • 25 Oct 2022 • Zhaoji Zhang, Qinghua Guo, Ying Li, Ming Jin, Chongwen Huang
Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem.
1 code implementation • 12 Oct 2022 • Yi Zeng, Minzhou Pan, Himanshu Jahagirdar, Ming Jin, Lingjuan Lyu, Ruoxi Jia
Most poisoning defenses presume access to a set of clean data (or base set).
no code implementations • 23 Feb 2022 • Zain ul Abdeen, He Yin, Vassilis Kekatos, Ming Jin
In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors.
2 code implementations • 17 Feb 2022 • Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
no code implementations • 11 Feb 2022 • Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
no code implementations • 20 Nov 2021 • Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li
To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.
2 code implementations • ICLR 2022 • Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, Ruoxi Jia
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
no code implementations • 29 Sep 2021 • Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.
no code implementations • 29 Sep 2021 • Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia
In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.
1 code implementation • 8 Sep 2021 • Fangda Gu, He Yin, Laurent El Ghaoui, Murat Arcak, Peter Seiler, Ming Jin
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
1 code implementation • 23 Aug 2021 • Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen
While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.
1 code implementation • 16 Jul 2021 • Hao Chen, Ming Jin, Zhunan Li, Cunhang Fan, Jinpeng Li, Huiguang He
Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.
no code implementations • 10 Jun 2021 • Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).
1 code implementation • 12 May 2021 • Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.
no code implementations • 2 May 2021 • Sarthak Gupta, Vassilis Kekatos, Ming Jin
The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.
3 code implementations • 27 Feb 2021 • Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu
Deep learning on graphs has attracted significant interests recently.
no code implementations • 25 Jan 2021 • Man Luo, Qinghua Guo, Ming Jin, Yonina C. Eldar, Defeng, Huang, Xiangming Meng
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.
1 code implementation • 16 Dec 2020 • He Yin, Peter Seiler, Ming Jin, Murat Arcak
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).
no code implementations • 25 Sep 2019 • Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
1 code implementation • 24 May 2019 • Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
no code implementations • 26 Oct 2018 • Ming Jin, Javad Lavaei
We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems.
no code implementations • 26 Dec 2015 • Ming Jin, Andreas Damianou, Pieter Abbeel, Costas Spanos
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
no code implementations • 22 Jun 2014 • Ming Jin, Han Zou, Kevin Weekly, Ruoxi Jia, Alexandre M. Bayen, Costas J. Spanos
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.